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Microsoft DP-100 Exam Dumps

Exam Name: Designing and Implementing a Data Science Solution on Azure Exam
Certification Name: Microsoft Azure

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  • Last Updation Date: 16-Apr-2024

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You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the
model as a real-time web service.
You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails
because an error occurs when the service runs the entry script that is associated with the model deployment.
You need to debug the error by iteratively modifying the code and reloading the service, without requiring a
re-deployment of the service for each code update.
What should you do?

 

Register a new version of the model and update the entry script to load the new version of the model from its registered path.

 

Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.

 

Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI

 

Add a breakpoint to the first line of the entry script and redeploy the service to AKS.

 

Create a local web service deployment configuration and deploy the model to a local Docker container.


Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI


Explanation
How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and
Azure Kubernetes Service (AKS) using Azure Machine Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API
using an Environment object as an input parameter. In this case our service will create a base docker image for
you during deployment stage and mount the required models all in one call. The basic deployment tasks are:
1. Register the model in the workspace model registry.
2. Define Inference Configuration:
a. Create an Environment object based on the dependencies you specify in the environment yaml file or use
one of our procured environments
b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.
3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).





You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website. Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?

 

attached Azure Databricks cluster

 

Azure Container Instance (ACI)

 

Azure Kubernetes Service (AKS) inference cluster

 

Azure Machine Learning Compute Instance

 

attached virtual machine in a different region


Azure Machine Learning Compute Instance


Explanation
Azure Machine Learning compute cluster is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network.





You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content
NOTE: Each correct selection is worth one point.



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